www.biogeosciences.net/11/4393/2014/ doi:10.5194/bg-11-4393-2014
© Author(s) 2014. CC Attribution 3.0 License.
Rapid formation of large aggregates during the spring bloom of
Kerguelen Island: observations and model comparisons
M.-P. Jouandet1, G. A. Jackson2, F. Carlotti1, M. Picheral3, L. Stemmann4, and S. Blain5,6
1Mediterranean Institute of Oceanography (MIO), Unité mixte: Aix Marseille Université – CNRS – IRD, 13288 Marseille
CEDEX 09, France
2Department of Oceanography, Texas A&M University, College Station, TX 77845-3146, USA 3CNRS, UMR7093, LOV, Observatoire océanologique, 06230, Villefranche/mer, France
4Sorbonne Universités, UPMC Univ Paris 06, UMR7093, LOV, Observatoire océanologique, 06230, Villefranche/mer, France 5Sorbonne Universités, UPMC Univ Paris 06, UMR7621, Laboratoire d’Océanographie Microbienne, Observatoire
Océanologique, 66650 Banyuls/mer, France
6CNRS, UMR7621, Laboratoire d’Océanographie Microbienne, Observatoire Océanologique, 66650 Banyuls/mer, France
Correspondence to:M.-P. Jouandet (marie-paule.jouandet@univ-amu.fr)
Received: 17 March 2014 – Published in Biogeosciences Discuss.: 28 March 2014 Revised: 24 June 2014 – Accepted: 28 June 2014 – Published: 20 August 2014
Abstract. While production of aggregates and their subse-quent sinking is known to be one pathway for the down-ward movement of organic matter from the euphotic zone, the rapid transition from non-aggregated to aggregated par-ticles has not been reported previously. We made one ver-tical profile of particle size distributions (PSD; sizes rang-ing from 0.052 to several millimeters in equivalent spheri-cal diameter) at pre-bloom stage and seven vertispheri-cal profiles 3 weeks later over a 48 h period at early bloom stage using the Underwater Vision Profiler during the Kerguelen Ocean and Plateau Compared Study cruise 2 (KEOPS2, October– November 2011). In these naturally iron-fertilized waters southeast of Kerguelen Island (Southern Ocean), the total particle numerical abundance increased by more than four-fold within this time period. A massive total volume increase associated with particle size distribution changes was ob-served over the 48 h survey, showing the rapid formation of large particles and their accumulation at the base of the mixed layer. The results of a one-dimensional particle dy-namics model support coagulation as the mechanism respon-sible for the rapid aggregate formation and the development of theVTsubsurface maxima. The comparison ofVTprofiles
between early bloom stage and pre-bloom stage indicates an increase of particulate export below 200 m when bloom has developed. These results highlight the role of coagulation in forming large particles and triggering carbon export at the
early stage of a naturally iron-fertilized bloom, while zoo-plankton grazing may dominate later in the season. The rapid changes observed illustrate the critical need to measure car-bon export flux with high sampling temporal resolution. Our results are the first published in situ observations of the rapid accumulation of marine aggregates and their export and the general agreement of this rapid event with a model of phyto-plankton growth and coagulation.
1 Introduction
Biological particle production and sedimentation out of the euphotic layer to underlying waters is the major mechanisms for atmospheric CO2removal and the redistribution of
car-bon and associated nutrients in the ocean. The fate of this ex-ported particulate carbon is a function of the plankton com-munity producing it in the upper layer and particle transfor-mation by microbes and zooplankton during their descent to the deep sea. Physical aggregation of particles is one key pro-cess in this transformation and transport and can explain the rapid formation and export of large particles during bloom conditions.
phytoplankton blooms. Since the HNLC regions result from low supplies of the crucial nutrient iron, the hypothesis is that these blooms are supported by natural sources of iron, most likely supplied from local islands and shallow sediment (Moore and Abbott, 2002; Tyrrell et al., 2005; Blain et al., 2007; Pollard et al., 2007).
The impact of iron on the biological carbon pump has been investigated in these natural bloom regions (Blain et al., 2007; Pollard et al., 2007) and in patches formed by adding iron to localized HNLC regions (Boyd et al., 2000, 2004; Gervais et al., 2002; Buesseler et al., 2004, 2005; de Baar et al., 2005; Hoffmann et al., 2006; Smetacek et al., 2012; Mar-tin et al., 2013). The observations made during the natural-iron fertilization programs KEOPS1 (Kerguelen Ocean and Plateau Compared Study) and CROZEX (CROZet natural iron bloom and EXport experiment) documented a twofold greater carbon export flux downward from the mixed layer (ML) in the naturally iron-fertilized bloom relative to that in unfertilized surrounding waters (Jouandet et al., 2008, 2011; Savoye et al., 2008; Pollard et al., 2009). An increase in Par-ticulate Organic Carbon (POC) flux after artificial fertiliza-tion experiments was detected only during SOFeX (Southern Ocean Fe Experiment; Buesseler et al., 2005) and EIFEX (European Iron Fertilization Experiment; Smetacek et al., 2012).
Optical examination of particles trapped in polyacry-lamide gels during KEOPS1 found that export at 100–430 m was dominated by fecal pellets and fecal aggregates (Ebers-bach and Trull, 2008), which can be considered as a form of indirect export. (Note that we consider direct export to be the flux of phytoplankton cells, either alone or in aggregates.) By contrast, the CROZEX experiment observed direct export of surface production by a diverse range of diatoms (Salter et al., 2007), consistent with phytoplankton aggregation en-hancing particulate flux. The lack of phytoplankton aggre-gation due to insufficient biomass has been invoked as the reason for which carbon export flux in SOIREE (Southern Ocean Iron Release Experiment) was not enhanced (Waite and Nodder, 2001; Jackson et al., 2005). The different re-sults for these systems reflect differences in physical forcing factors, experimental duration, and seasonal evolution of the biological community.
Because of the complexity of the export system, there are still extensive unknowns about the effect of iron fertiliza-tion on carbon export from the surface to the bottom layer. The aim of our study is to investigate processes responsi-ble for the formation of large particles (>52 µm) on a short timescale during bloom development in the surface ML.
We combine multiple vertical profiles of large-particle size spectra collected over a relatively short period during KEOPS2 with a one-dimensional particle dynamics model that incorporates phytoplankton growth as a function of light and nitrate concentration and coagulation as function of ag-gregate size. We measured particle distributions using the Underwater Vision Profiler (UVP) deployed at a bloom
sta-tion above the Kerguelen plateau under pre-bloom condista-tions and at an early bloom stage during a period of rapid change. The coagulation model used here is an extension of a zero-dimensional model that simulates abundances of phytoplank-ton cells in the surface mixed layer as well as the size distri-butions of settling particles (e.g., Jackson et al., 2005; Jack-son and Kiørboe, 2008). Here it has been extended into a one-dimensional model to describe the vertical distribution of phytoplankton in the mixed layer and the formation, dis-tribution, and flux of aggregates. The comparison between observed and modeled particle size distribution provides a unique opportunity to test the usefulness of the coagulation theory to explain rapid formation of large aggregates during the early stage of a phytoplankton bloom.
2 Material and methods 2.1 Field measurements
Station A3 (50◦380 S, 72◦050 E), located above the Ker-guelen plateau, is characterized by a weak current (speed<3 cm s−1; Park et al., 2008b), which results in a
wa-ter mass residence time of several months. This long resi-dence time allows the bloom to develop and persist over an entire season in response to natural-iron fertilization (Blain et al., 2007). During KEOPS2, Station A3 was sampled first during pre-bloom conditions on 21 October 2011 (A3-1) and was revisited during the early bloom from 15 to 17 November (A3-2), 2 weeks after the bloom had started. High sampling frequency started during the second visit at midnight on 15 November (Table 1).
The Underwater Vision Profiler 5 (UVP 5 Sn002) used in the present study was a component of the rosette profiler sys-tem. The UVP5 detects and measures particles larger than 52 µm on images acquired at high frequency (Picheral et al., 2010). Images were taken and data recorded at a frequency of 6 Hz, corresponding to a distance of 20 cm between images at the 1 m s−1lowering speed of the conductivity–temperature–
depth (CTD) system. The observed volume per image is 0.48 dm3; the total volume sampled for the 500 m depth pro-files at Station A3 was 1.2 m3. The instrument takes a digi-tal picture of a calibrated volume lit from the side. The im-age is scanned for particles and particle dimensions are mea-sured. The pixel area (Sp)for each object is converted to
cross-sectional area (Sm)using the calibration equationSm=
0.00018S1.452
p . An equivalent spherical diameterd is
calcu-lated for that cross-sectional area. Hydrographic and biogeo-chemical properties, including density, fluorescence, and tur-bidity (as determined by a transmissometer using a wave-length of 660 nm and a 25 cm path wave-length), were measured simultaneously with a conductivity–temperature–depth sys-tem (Seabird SBE-911+CTD) linked to a Seapoint Chelsea
Table 1.Date and time of the casts performed at Station A3.
Station Date Time (LT) Mixed layer (dd-mm-yyyy) (hh : mm) depth (m)
A3-1 21-10-2011 02:20 165 A3-2/1 15-11-2011 23:20 143 A3-2/2 16-11-2011 07:50 171 A3-2/3 16-11-2011 11:30 138 A3-2/4 16-11-2011 19:15 147 A3-2/5 17-11-2011 01:10 123 A3-2/6 17-11-2011 05:30 163 A3-2/7 17-11-2011 14:30 124
We also present selected results of chlorophylla (Chla) and nitrate concentrations, as well as relative biomass of dif-ferent phytoplankton size classes. Chlaand pigment concen-trations were measured using high-performance liquid chro-matography (HPLC) following the method described in Las-bleiz et al. (2014); the fraction of a phytoplankton group rel-ative to the total biomass was calculated using the model of Uitz et al. (2006). Nitrate was analyzed with a Technicon Au-toAnalyzer as described in Tréguer and LeCorre (1975).
2.2 Data processing
The particles in each 5 m depth interval, with depth de-termined from the associated CTD measurements, were sorted into 27 diameter intervals (from 0.052 to 27 mm, spaced geometrically), and concentrations were calculated for each diameter and depth interval. We further analyzed size spectra having a minimum of five particles per size bin and depth interval; this criterion eliminated bins with d >1.6 mm. The depth distributions of particles are summa-rized in terms of their total numberNT(# L−1)and volume
VT(mm3L−1=ppm) concentrations.
Particle number distributions (n)were calculated by divid-ing the number of particles (1N )within a given bin by the width of the ESD (Equivalent Spherical Diameter) bin (1d) and the sample volume. The resulting units are # cm−4. The
distributions are usually plotted in a log-log plot because of the large ranges innandd. To compensate for these ranges, the results are often displayed asnVdspectra, wherenis mul-tiplied by the median diameter (d)and the spherical volume V =π/6d3for the particle size range. This form of the
parti-cle size distribution has the advantage that the area under the curve is proportional to the total particle volume concentra-tion when nVdis plotted against log(d). The carbon export flux FPOC (mg C m−2d−1)can be estimated from the size
spectra using the following empirical relationship:
FPOC=Adb, (1)
whered is the diameter in millimeters,A=12.5, andb=
3.81 (Guidi et al., 2008). Guidi et al. (2008) developed the relationship by comparing particle size spectra to sediment
trap collection rates at locations around the world. The value ofbis less than the value of 5 expected for spherical particles of constant density (for which mass increases asd3and sink-ing speed asd2). It is consistent with marine aggregates hav-ing increashav-ing porosity with increashav-ing size (e.g., Alldredge and Gotschalk, 1988).
2.3 Model equations and parameterization
The biological model describes the growth rate of phyto-plankton in the water column as a function of light and nu-trient (nitrate) concentration. The model uses a maximum phytoplankton specific growth rate Gmax=0.45 d−1
(Tim-mermans et al., 2004; Assmy et al., 2007). Phytoplankton cells are transformed into aggregates by differential settling and shear using the standard coagulation model of Jackson (1995). Aggregates are also fragmented into two similar parts using size-dependent disaggregation rates (Jackson, 1995). The primary phytoplankton cells are chosen to match the size ofFragilariopsis kerguelensiswhich was the dominant species under pre-bloom conditions (L. Armand, personal communication, 2014). A single phytoplankton cell hasd1=
20 µm, a density of 1.0637 g cm−1, and a resulting settling
speed ofv1=1.05 m d−1. The probability that two particles
colliding stick together,α, is assumed to be 1. The average turbulent shear rate isγ=1 s−1(Jackson et al., 2005). The
initial abundance of phytoplankton is 10 cells cm−3. These
and other parameter values are shown in Table 2. The one-dimensional model simulates the distribution of particles of different sizes, including solitary phytoplankton cells, and ni-trate concentrations at 2 m depth intervals within the 0–150 m layer. This depth range corresponds to the average surface ML thickness during the survey (Table 1). Neither zooplank-ton grazing nor particle transformation by bacterial processes is included in these simulations. The model is described in greater detail in Supplement S1.
While the concept of spherical diameter is simple for a solid sphere, it is not for irregular marine aggregates, with different shapes, assembled from multiple sources, hav-ing water in the interstices between their components and yielding different sizes for different measurement techniques (e.g., Jackson, 1995). The simplest diameter is the conserved diameterdc, i.e. the diameter if all the solid matter were to
be compressed into a solid sphere. It has the advantage that when two particles collide and form a new particle, the con-served volume of the new particle is the sum of the concon-served volumes of the two colliding particles. The particle diameter d, determined by the UVP, is larger thandc because
aggre-gate size is determined from the outer shape of the aggreaggre-gate and thus contains pore water between source particles. The relationship between the two measures of particle diameter is described using the fractal dimension (see Supplement S1). The model calculations use dc. However, all model results
shown here use the apparent diameterda, which is used to
Table 2. Symbols and parameter values used for the model. Conversion constants include the following: carbon to chlorophyll – 50 g C : g Chla; carbon to nitrogen – 106 mol C : 16 mol N.
Symbol Quantity Value Units Reference
dc Conserved diameter cm da Apparent diameter cm d1 Median algal diameter 20 µm Dfr Fractal dimension 2 – G Specific growth rate d−1
Gmax Maximum specific growth rate 0.45 d−1 Timmermans et al. (2004)
I Irradiance ly d−1
Io Surface irradiance ly d−1 Evans and Parslow (1985)
k Total light attenuation –kw+krP m−1
kr Coefficient for light attenuation by plants 0.03 m2(mmol N)−1 Fasham et al. (1990) kw Light attenuation of water 0.04 m−1 Fasham et al. (1990) Kd Half saturation constant 1 mmol N m−3 Fasham et al. (2006) Kz Eddy diffusivity 100 m2d−1 Park et al. (2008a)
m Particle mass g
n(d) Number spectrum for diameterd cm−4 n(m) Number spectrum for massm cm−3g−1 N Nitrate concentration mmol N m−3 Qi Particle mass inith section g
r Phytoplankton mortality rate 0.04 d−1 Assmy et al. (2007) rp Relative light limitation –
rn Relative nitrate limitation – vi Settling velocity for particle inith section m d−1
V Particle volume
αI Slope of photosyn. curve 0.04 ly−1 Evans and Parslow (1985)
α Stickiness 1 – Jackson et al. (2005) β Coagulation kernels
1β
i,j,l,2βi,l, Sectional coefficients
3β
l,l,4βi,l
φ Phytoplankton concentration mmol N m−3
λi Disaggregation coef. forith section d−1 Jackson (1995)
γ Fluid shear 1 s−1 Jackson et al. (2005) µ Average algal growth rate d−1
da is calculated fromdcusing the fractal relationship and a
fractal dimension of 2 (Supplement S1). Note that reported values of the fractal dimension vary widely, from 1.3 to 2.3 (Burd and Jackson, 2009). The value of 2 used here is in this range and yields peaks in thenVddistributions similar to those determined from UVP measurements, unlike values of 2.1 and 1.9 (not shown).
3 Results 3.1 Observations
3.1.1 Biogeochemical and physical context
The water column was characterized by a deep mixed layer (∼150 m) during the pre-bloom and early bloom surveys,
with a range of 120 to 171 m (Figs. 1 and 2). Isopycnal
Figure 1.Vertical distribution of sigma (black line), fluorescence (green line), and turbidity (blue line) (A)and vertical profiles of total abundance (NT)and total volume (VT)at the first cast of A3, during winter (A3-1, 21 of October)(B).
fluorescence (Figs. 1 and 2). The two were, in fact, highly correlated in the surface mixed layer (r=0.95), which was
not always the case in deeper layers. Nitrate concentrations at A3-1 were 28 to 30 µM in the mixed layer and then decreased by 4 µM at A3-2 (Fig. 3a). Pigment analysis (Fig. 3) and cell counts of phytoplankton captured in nets (L. Armand, personal communication, 2014) showed that the phytoplank-ton community was dominated by diatoms –Fragilariopsis at A3-1 and an assemblage of Fragilariopsis,Chaetoceros , and Pseudo-nitzschia at A3-2. The zooplankton commu-nity was dominated by copepods with a mixture of adult (50.5 %) and copepodites stage (49.5 %) at A3-2 (Carlotti et al., 2014). Zooplankton biomass increased from 1.4 g C m−2
at A3-1 to 4.1 g C m−2at A3-2 over the 0–250 m layer, and
was thus more than twofold lower than the mean biomass of 10.6 g C m−2measured at A3 in summer during KEOPS1
(Carlotti et al., 2008).
3.1.2 Evolution of the total abundance and volume distributions in the mixed layer
In the pre-bloom profile, total particle abundance (NT)and volume (VT)distributions at Station A3 were characterized
by a two-layer structure (Fig. 1b). The shallower layer had relatively uniform NT (VT) values of 90±5 particles L−1
(0.3±0.1 mm3L−1)between 0 and 100 m; the second layer, from 100 m to the base of the ML (166 m), had subsurface NTandVTmaxima of 142 particles L−1and 0.45 mm3L−1.
There was a twofold increase in NT at the first cast of the
early bloom (A3-2/1), with values reaching 200±7 # L−1
in the first 100 m and a subsurface maximum of 300 # L−1
(Fig. 4).VTalso increased by one order of magnitude
reach-Figure 2.Temporal evolution of density(A), fluorescence(B), and turbidity(C)during the spring survey. The red line shows the mixed layer depth.
Figure 3.Vertical distribution of the concentration of NO3(A); total Chla(Tchla), andTchlaassociated with micro-(Tchlamicro),
nano-(Tchlanano), and picophytoplankton (Tchlapico)(B). The filled
sym-bols indicate pre-bloom stage; the hollow symsym-bols indicate early bloom stage.
ing a value of 3 mm3L−1at the depth of the subsurface
max-ima (Fig. 4). In subsequent casts, there was a 40 m thick sur-face layer with constantNT andVTand a subsurface
Figure 4.Vertical distribution ofNTandVTfor the different casts
during early bloom stage.
the rapid and continuous increase of bothNT andVT from
A3-2/1 to A3-2/5 over a roughly 24 h time period. This was more than a redistribution of aggregates, asNT andVT
in-tegrated over the ML increased from 282 to 743 # m−2and
from 101×103 to 1500×103mm3m−2. There was a
fur-ther increase by the end of the survey in the maximum VT
to 25 mm3L−1, almost two orders of magnitude greater than
for the pre-bloom situation.
3.1.3 Evolution of size distributions with depth and time during the early bloom phase
The particle size distributions (PSD) calculated from the UVP observations provide additional insight into the change in particle abundance during the 2 d spring observation pe-riod. In order to display the vertical structure of PSD, we compare the average over the nominal euphotic zone (0 to 40 m) to the average over the 40 m layer centered on the sub-surface particle maximum. Particles larger than 129 µm were more abundant in the subsurface layer (Fig. 5a). Consistent with the analysis in the previous section, the smallest differ-ence between the two layers occurred during the pre-bloom sampling (A3-1). The maximum increases were in the 0.128– 0.162 mm and 0.204–0.257 mm size classes, with abundance increases of 66 # L−1and 62 # L−1for A3-2/3. The increase
was also substantial in the 0.4–0.5 mm size range. The cumu-lative volume distribution in the 0–40 m euphotic zone shows that increased particle volumes resulted from formation of larger particles (Fig. 5b).
Within the vertical particle maxima, half of VT was in
particles withd >0.5 mm at the start of the survey, while these larger particles provided more than 80 % at the end. The largest change in size spectra was in the approximately 17.5 h period between the morning (A3-2/2) and the middle of the night (A3-2/5) of 16 November.
ThenVd size distribution for profile A3-2/5 is shown in detail in Fig. 6. The area under the curve at a constant depth is proportional to the particle volumeVTat that depth.
Be-tween the surface and 60 m most particle volume is made up of the smallest size class with particlesd ranging between 200 and 500 µm. Massive changes occurred with depth with an increase of the volume andd. The volumes from 60 m to 150 m are supported by larger particles ranging between 0.65 mm and 1.1 mm, with a peak of 30 ppm for adof 1 mm.
3.1.4 Particle distributions below the ML
In the first 50 m below the ML, VT values mirrored those
in the overlying waters, increasing to 20 ppm by the end of the survey period (A3-2/7) (Fig. 7).VT decreased from
the base of the ML to 200 m by about a factor of 20 for A3-2/6 and A3-2/7. Below 200 m, the depth limit for win-ter mixing, there was no change inVTduring the 2 d survey.
The averageVTwas 0.40±0.10 and 0.38±0.10 mm3L−1at
250 and 350 m. There was an increase inVTat about 475 m
caused by resuspension from the bottom, as documented dur-ing KEOPS1 (Chever et al., 2010; Jouandet et al., 2011). The particle number distribution (n) decreased from the base of the mixed layer to 350 m in all size classes, particularly for particles larger than 500 µm, which were no longer detectable (Fig. 7b).
3.1.5 Relationship between particle volume and fluorescence
As mentioned, there was no simple correlation betweenVT
and fluorescence. However, separating the observations by depth layers (the mixed layer, the base of the ML to 200 m and deeper than 200 m) reveals a pattern (Fig. 8). In the shallowest layer, there was an increase from the pre-bloom values of low fluorescence and particle volume for A3-1 (2A3-1 October) to high fluorescence and low particle vol-ume for A3-2/1 (15 November, 23:20 LT). This is consis-tent with an increase in phytoplankton biomass without ag-gregate production. For A3-2/2, there are hints of an in-crease inVT, which became pronounced in subsequent casts.
The increased particle concentrations were accompanied by a slight decrease in fluorescence. For the seven casts performed during the early bloom stage, the correlations between flu-orescence and VT were negative (−0.53), with a slope of −0.015 µg Chl mm−3. In the second layer, immediately
be-low the surface mixed layer, fluorescence andVT increased
Figure 5.Difference of the size spectra abundance between the depth of the volume maxima (Zmax)and the euphotic layer (Ze)(A)and cumulative volume distribution(B)in the euphotic layer (dashed line) and at the depth of theVTsubsurface maxima (solid line).
Figure 6. Volume distribution size spectra along vertical axis on the 17 of November at 01 : 10 LT (A3-2/5). The white line indicates values at 150 m, the bottom of the model regime.
no phytoplankton growth in this depth layer, but with phy-toplankton and aggregates arriving together from above, pre-sumably in aggregates. There was no correlation between flu-orescence andVTbelow 200 m during this period.
3.1.6 POC flux
The flux at 200 m computed from the UVP particle size dis-tributions increased from 1.8 mg m−2d−1during pre-bloom
conditions to 23 mg C m−2d−1during the early bloom (last
cast of the survey). This increase over time as estimated from UVP measurements was also evident at 400 m but with a smaller change, with FPOC increasing from 1.04 to
3.5 mg C m−2d−1at 400 m (Table 3).
Our POC flux estimates at 200 m for the spring bloom are in the range of the POC flux estimated from the sedi-ment trap PPS3/3 (27±8 mg C m−2d−1)and below the es-timates made from the gel trap (FPOC=66 mg C m−2d−1)
and from the thorium deficit (FPOC−Th=32 mg C m−2d−1)
(Laurenceau et al., 2014; Planchon et al., 2014).FPOC−That
100 m increased from pre-bloom to early bloom but stayed unchanged at 200 m. TheFPOC−That 200 m was estimated at
A3-2/1, consistent with UVP observations that did not record anyVTincrease.
3.2 Simulations
3.2.1 Development of the phytoplankton bloom
The phytoplankton in the model grew exponentially in the upper part of the water column for the first 8 d of the sim-ulation, slowing down as light limitation became important (Fig 9a). The specific rate of integrated population growth (0 to 150 m) was∼0.06 d−1for this initial period. The peak
phytoplankton biomass was 2 µg Chl L−1at about 10 m depth
on day 13. The phytoplankton biomass decreased slightly when coagulation became an important removal mecha-nism by day 20, with surface phytoplankton biomass of 1.7 µg Chl L−1, a maximum concentration of 1.9 µg Chl L−1
at 15 m, and a minimum concentration of 0.2 µg Chl L−1at
150 m. Surface nitrate concentrations decreased from the ini-tial 30 to 25 µM by day 20 (Fig. 9b).
3.2.2 Development of the aggregate volume
Aggregates withda>100 µm appeared by day 14, when the
Table 3.Comparison of the POC fluxes (FPOCin mg m−2d−1)derived from particle size distributions from the UVP, particle distributions from gel-filled sediment traps and sediment trap PPS3/3 Technicap Inc, France (Laurenceau et al., 2014) during KEOPS2 and KEOPS1 (Jouandet et al., 2011, Ebersbach and Trull, 2008).
Winter Spring Midsummer End summer KEOPS2 KEOPS2 KEOPS 1 KEOPS1
FPOCat 200 m F=Adb 1.75 23.11 869 58 (mg m−2d−1) Gel trap 66
Trap PPS3 27±8
FPOCat 350 m F=Adb 1.04 3.50 326 67
(mg m−2d−1)
Figure 7.Distribution ofVTbelow the surface mixed layer(A). Normalized particles size spectra abundance average over the 320–350 m layer (dotted line) and 100–200 m layer (solid line)(B).
slightly in the upper 50 m and the aggregates at the base of the mixed layer slowly decreasing. The vertical size distribu-tion at day 20 provides further details on the system (Fig. 10). The nVda size distribution shows the distribution of parti-cle volume, with the area under the curve being proportional to the particle volume when displayed with a logarithmicda
axis, as here (Fig. 10). Most particle volume at the surface is made up the smallest particles, the single phytoplankton cells. At 10 m depth, aggregates appear with a maximum nVda value atda=200 µm. With increasing depth, the
to-tal volume and the diameter of the maximumnVdaboth
in-crease. Thedaat the maximum became 0.9 mm at about 70 m
depth, remaining constant with increasing depth, even though the total volume continued to increase with depth.
4 Discussion
Figure 8.Scatter plots of fluorescence andVTfor the three layers:
surface to base of ML(A), base of ML to 200(B), and>200 m (C). Large symbols indicate the means for a profile in the panel depth range.
Coagulation theory has been used to predict the maximum phytoplankton biomass in the ocean (e.g., Jackson and Kiør-boe, 2008). Coagulation of phytoplankton cells is a nonlin-ear process. Rates increase dramatically as phytoplankton biomass increases, eventually removing cells as fast as they divide. The volume concentration at which this occurs is the critical volume concentration (Jackson, 2005):
Vcr=π µ(8αγ )−1. (2)
For this calculation, we assumed an average specific growth rate for the population increase rate µ=0.1 d−1, in
agree-ment with measureagree-ments made by Closset et al. (2014), α=1, andγ=1 s−1. Note that the average increase rate is
not the same as the peak rateGmax. For a POC : volume
ra-tio of 0.17 g C cm−3(Jackson and Kiørboe, 2008) and a
car-bon to chlorophyll ratio of 50 g C : g Chl, this is equivalent to 1.5 µg ChlaL−1. This value forVcris remarkably close to
the maximum concentrations of 2–2.2 µg ChlaL−1observed during the particle formation at A3-2.
Figure 9.Model results for vertical distribution through time of phytoplankton (µg Chl L−1)(A; phytoplankton concentration does
not include any algae present in aggregates), nitrate (µM)(B), and VTa(ppm)(C). Contour interval is 0.1 µg Chl L−1(A), 0.5 µM(B),
and 1 ppm(C). The calculation assumes that the UVP only mea-sures aggregates larger than 100 µm.
The rapid production of aggregates at Station A3 observed in this study provides an impressive example of the impor-tance of coagulation in controlling PSD and vertical export of primary production.
The nature of the exported material collected in a free-drifting sediment gel trap at 210 m supports also the impor-tance of algal coagulation in forming the exported material (Laurenceau et al., 2014). Their analysis shows that the par-ticle flux number and volume were dominated by phytoag-gregates over the 0.071–0.6 mm size range.
4.2 Limitations of the model
Figure 10. Distribution of apparent particle volume, nVda, as a
function of depth anddaas calculated by the model at 20 d. Because
the value ofdais plotted on a logarithmic scale, the area under the
curve for each depth is proportional to total particle volumeVTa.
mixing coefficient (1000 m2d−1)yield a smaller difference
in chlorophyll between the surface and 150 m, but there is still a difference of 0.8 µg Chl L−1 over the depth range
(results not shown). The vertical mixing rate estimated for the iron fertilization experiment EIFEX, 29 m2d−1, was
ac-tually smaller than that used in these simulations, namely 100 m2d−1 (Smetacek et al., 2012). A previous model of
phytoplankton growth in the Kerguelen region discussed large-scale horizontal patterns but unfortunately did not dis-play vertical distribution (Mongin et al., 2008). Whatever the reason for the relatively uniform fluorescence profile, it is not simply a faster diffusive mixing rate. Those differences illustrate the difficulty of building a realistic phytoplankton growth model in the region to drive the coagulation model. The shallower phytoplankton distribution does affect the dis-tribution of aggregates as well.
In a model such as the one used in the present study, there are many parameters and modeled processes that influence the final results. These include parameters such as the fractal dimension, the size of the phytoplankton cells, or processes to describe diatom chain growth, disaggregation rates, and grazing. While the parameters could be tuned systematically to give an improved fit, what is striking is the similarity be-tween observations and the model without such a systematic fitting procedure. One important parameter that was varied during model development to adjust the results was the frac-tal dimension. Decreasing it decreased the diameter of the peak value ofnVd. The value that was chosen,Dfr=2, was
similar to some of the estimates of fractal dimension noted above and did provide the correctnVddistribution when co-agulation occurred.
Other processes are known to affect particle concentra-tions and fluxes, most notably physical process such as ad-vection and biological processes such as zooplankton grazing
and fecal pellet production (e.g., Lampitt et al., 1993; Stem-mann et al., 2000; Turner et al., 2002). The importance of advection could be inferred from time series measurements of LADCP (Lowered Acoustic Doppler Current Profiler). The results indicated a current below 0.1 m s−1, with
neg-ligible changes over the survey in the 0–200 m depth layer (Y. H. P. Park, personal communication, 2014). The abun-dance and volume of zooplankton larger than 0.7 mm, as well as fecal sticks/pellets and aggregates, were estimated from the identification of organisms in the vignettes recorded by the UVP using the Zooprocess imaging software (see Picheral et al., 2010). The volume of copepods did not in-crease through the early bloom survey, suggesting that they were not responsible for the observed rapid increase in par-ticles. Ingestion rates were also estimated from zooplank-ton biomass using the relationship detailed in Carlotti et al. (2008) using the biomass results integrated over the 0– 250 m layer. The ingestion rate was 1.36 mg C d−1 during
the early bloom cast and lower than during the KEOPS1 summer cruise. In addition, fecal pellet production should have a diurnal signal (Carlotti et al., 2014), which was not observed in the VT profiles. Lastly, fast-sinking fecal
pel-lets are much smaller than the aggregates observed here. For example, fecal pellets falling at 100 m d−1 are typically 2–
5×106µm3, equivalent tod=200 µm (Small et al., 1979),
compared to the millimeter-sized aggregates dominating at A3. Thus, changes in zooplankton populations can be ruled out as an explanation of the observedVTincrease at this time,
although not through the entire season. Modeling the dynam-ics of the entire season would require integrating zooplank-ton activity.
4.3 Comparison with other studies 4.3.1 KEOPS1
The comparison of our results with the size spectra obtained from UVP measurements at Station A3 during the early bloom (KEOPS2) and the late stage of the bloom (KEOPS1) allows us to investigate the seasonal variability of particle production in the 0–200 m layer and the POC export flux (Fig. 11, Table 3). During summer (KEOPS1), the phyto-plankton community was also dominated by Chaetoceros but shifted toEucampia antarctia by the end of the bloom (Armand et al., 2008). Zooplankton abundance was tenfold higher than during the early bloom and the community was dominated by copepods at copepodite stage (Carlotti et al., 2008). The mixed layer decreased from 150 m during early bloom to 70 m during summer. During KEOPS2, VT
in-creased more than twentyfold from pre-bloom conditions, probably as a result of the higher diatom biomass (L. Ar-mand, personal communication, 2014), and coagulation as described in Sect. 4.1. The value ofVTachieved by the time
Figure 11. (A, B) Comparison of the total volume profiles mea-sured during KEOPS2 in October (A3-1, blue), November (A3-2/7, green), and during KEOPS1 in January (red) and February (brown). The depth scale for(B)is enlarged and covers only 200–500 m.(C, D)Comparison of the normalized size spectra in the 0–200 m(C) and 200–400 m layer(D). The colors indicate the same profiles as in(A, B).
vertical structure was different, with two subsurface maxima during KEOPS1, the first one present at the base of the ML (70 m). The larger VT measured in January was associated
with an increase in the fraction of large particles (Fig. 11c). Below 200 m depth,VTwas still 10 times higher during
the peak bloom as compared to early bloom. This resulted in forty- (at 200 m) and tenfold (at 400 m) higher carbon export fluxes during the peak bloom than the early bloom (Table 3). During the decline of the bloom,VTand POC flux were still
higher than during early bloom. This is consistent with the general scheme of low-production–high-export at the end of the bloom put forward by Wassmann (1998). Our results pro-vide insights into particle production and size distributions at different stages of the seasonal bloom. The early bloom occurs before zooplankton grazing dominates. This leads to a large increase in diatom abundance resulting in rapid ag-gregate formation and export from the surface ML. Later in the season, export becomes controlled by zooplankton graz-ing and fecal pellet production, as found through the gel trap analysis (Ebersbach and Trull, 2008). Despite the importance of zooplankton grazing in the late season, the presence of VTmaxima at the base of the ML indicates that coagulation
still occurred during summer. An increase of aggregate for-mation through coagulation as a result of high cell numbers in the ML and their disappearance due to grazing between the base of the mixed layer and 200 m traps could also
ex-plain the dominance of fecal aggregates in the gel traps dur-ing the summer deployments. Combindur-ing KEOPS cruises to describe temporal scales of particle production and export (transient versus seasonal) is useful as a first step, but our limited observations highlight the need for high-frequency data collection over long periods.
4.3.2 Potential impact of coagulation after iron fertilization
Our results can be compared to those from other iron fer-tilization experiments to understand the relative roles of co-agulation and zooplankton grazing on particle export during different parts of the bloom cycle. However, it must be re-membered that fertilization experiments differ in important aspects, including location, physical and chemical regimes, and observational techniques applied to determine stocks and fluxes. In addition, conclusions about carbon export from the surface often depend on sediment traps that are usually lo-cated well below the euphotic zone or surface ML, sampling events that have been filtered by intervening processes and offset by transit times. With this in mind, we compare our results to those from other iron fertilization studies by clas-sifying them into those with phytodetritus export driven by diatoms on the one hand and the rest, including those with a zooplankton-mediated export, on the other hand.
The artificial iron fertilization experiment SOIREE (February 1999) found an increase in phytoplankton biomass (Chla=2 mg m−3)as a result of the iron addition, but no rapid removal of phytoplankton production. The export flux was low and driven by phyto-detrital aggregates (Waite and Nodder, 2001). Jackson et al. (2005) argued that the final abundance of phytoplankton cells was too low for rapid co-agulation and sinking. There was a change in diatom settling rate associated with a change in iron status. The persistence of the bloom after iron was depleted implies that zooplankton grazing was not removing the particulate material.
The EIFEX (February–March 2004) environment was remarkably similar to that of KEOPS2 (Smetacek et al., 2012). The mixed layer was slightly shallower during EIFEX (100 m) than during KEOPS2 (150 m), but still relatively deep; the phytoplankton accumulation rates were also similar (0.03 to 0.11 d−1). Iron fertilization stimulated a large diatom
bloom that reached concentrations of about 2 mg Chlam−3 4 weeks after the fertilization started. There was little effect on vertical export during the first 4 weeks, but export then increased rapidly to 110–140 mmol C m−2d−1. This change
was associated with mass mortality of several diatom species that formed rapidly sinking, mucilaginous aggregates of en-tangled cells and chains (Smetacek et al., 2012). This pattern of rapid formation of algal cells late in the bloom is similar to what we observed.
2005) (Venables et al., 2007). Carbon export fluxes estimated from a sediment trap in the highly productive, naturally iron-fertilized region of the subantarctic waters were two to three times larger than the carbon fluxes from adjacent HNLC wa-ters (Pollard et al., 2009). Vertical flux was dominated by a diverse range of diatoms, which suggests an important role for direct export, such as by coagulation.
In contrast, the particulate flux project in a region of el-evated biomass (Chl a= 1.9 mg m−3) in the Subantarctic Zone east of Tasmania fueled by enhanced iron was domi-nated by fecal aggregates (Ebersbach et al., 2011).
The LOHAFEX (loha is the Hindi word for iron) iron fertilization experiment was one of the few to use a parti-cle measuring system for the water column, also the UVP (Martin et al., 2013). A cyclonic eddy low in silica on the Antarctic Polar Frontal Zone was fertilized with iron. In response, phytoplankton biomass almost doubled to 1– 1.5 mg Chl am−3, but 90 % of it was in flagellates of less
than 10 µm instead of diatoms. There was no observable change in concentrations of particles larger than 100 µm or in vertical particle flux. There were several reasons proposed for the low export, including the lack of diatoms in the low-silicate water and intense particle consumption, particularly at the base of the mixed layer (66 m).
5 Conclusions
It is clear that particle flux in the ocean is the result of many interacting processes, and none of these has been identified as dominant across systems. In the present study, we were able to observe rapid aggregate formation and sedimentation of high concentrations of diatoms from the euphotic zone. Our observations are consistent with results from a one-dimensional model that includes only phytoplankton growth and coagulation. Our results demonstrate the utility of co-agulation theory in understanding vertical flux and its im-portance in initiating the formation of large particles in the mixed layer and their subsequent transfer to depth during a bloom. Nevertheless, efforts are still required to measure large aggregates distribution at a high frequency to fill the temporal window between these short time events taking place during the early bloom and the possibly slower dynam-ics of summer. In addition, more effort is required to under-stand vertical variations better at a fine scale for all times and particularly to estimate the transformative roles of microbes and zooplankton in decreasing the total particle volume ex-ported from the euphotic zone.
The Supplement related to this article is available online at doi:10.5194/bg-11-4393-2014-supplement.
Acknowledgements. Thanks to the Kerguelen Ocean and Plateau Compared Study (KEOPS2) shipboard science team and the officers and crew of R/V Marion Dufresne for their efforts. Christine Klaas provided helpful input on biological conditions. We acknowledge the constructive comments by E. Laurenceau. This research was supported by the French Agency of National Research grant (# ANR-10-BLAN-0614). G. A. Jackson was supported by US National Science Foundation (NSF) grant OCE09-27863. L. Stemmann was supported by the chair VISION from CNRS/UPMC.
Edited by: G. Herndl
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